From the CoreCast
engineering team.

After reviewing over 200 production deployments, pipeline reliability problems cluster around a surprisingly small set of root causes. Here's what we found — and what the highest-reliability teams do differently.

Multi-cloud doesn't have to mean trading one form of lock-in for infrastructure chaos. Here's how to design for portability without creating a second problem — and where most teams get it wrong.

Slow dashboards aren't a design or frontend problem — they're almost always a data problem. Here's how to diagnose which of the four common causes is yours, and fix it.

Eight patterns that show up in every serious production streaming pipeline — tumbling windows, session windows, dead letter queues, CDC — with honest notes on where each one will get you into trouble.

Building your own data platform sounds like control and flexibility. In practice, it's a multi-year tax on every engineering team in the company. Here's the honest case against doing it.

P95 query latency was 9.4 seconds. Six weeks later it was 1.8 seconds. A detailed walkthrough of what we changed — data model, partition strategy, pre-aggregation — and why each fix mattered.

Analytics stacks don't fail catastrophically — they degrade slowly. Five concrete signals that the slow grind has crossed into territory where incremental fixes won't get you where you need to go.

These two terms are often used interchangeably by people who should know better. They solve different problems and assume different organizational structures. Choosing the wrong one costs 18 months.

Batch feels cheap because the compute cost is on the invoice. What isn't: stale decisions, engineer hours absorbed by failing jobs, excess storage, and coordination overhead. Most teams have never added it up.

Your pipeline handles 10,000 events per second without breaking a sweat. Then you hit 800,000. Backpressure, schema drift, hot partitions, state explosion — the failure modes that only reveal themselves under real load.